Network Enabled Operations: The Experiences of Senior Canadian Commanders
Bibliographic record
Abstract
In order to fully understand the nature of Networked Enabled Operations (NEOps) today, how Canadian networked operations differ from those in other countries and how NEOps might evolve in the future, it is essential to provide context for and to document recent Canadian experiences with networked operations. However, to date, very little has been written on the Canadian experience with NEOps, particularly at the operational level of command. A recent DRDC Contract Report, Beware of Putting the Cart before the Horse: Network Enabled Operations as a Canadian Approach to Transformation, provided some context for NEOps and noted that Canada has made significant contributions to the evolution of networked operations. It also noted that these contributions have not been well documented. This report begins the documentation of recent Canadian experiences with networked operations based on an analysis of interviews conducted during January and February 2006 with eight Canadian commanders who had recent experience with networked operations at the operational level of command. The analysis begins with a context for understanding NEOps; it then presents key issues raised in the interviews in a thematic format; and the analysis concludes by summarizing and synthesizing the key issues raised in the interviews.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.036 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".